Forward Velocity Research Articles

Visual assessment of cerebrospinal fluid flow dynamics using 3D T2-weighted SPACE sequence-based classification system.

Flow-related signal void artifacts can be visualized on the T2-weighted (T2W) three-dimensional sampling perfection with application-optimized contrast (3D-SPACE) sequence. Flow void artifacts in the cerebral aqueduct and the fourth ventricle can provide information about cerebrospinal fluid (CSF) flow dynamics. In this study, we aimed to test the performance of the T2W 3D-SPACE sequence in assessing the CSF flow in the aqueduct and/or fourth ventricle. A total of 137 patients (age range = 3-89 years) who underwent CSF flow study were included. The amount of signal loss on T2W 3D-SPACE due to flow in the aqueduct and fourth ventricle was assessed and graded using a 4-point scale of 0 (absence of flow void) to 3 (signal void filling the aqueduct and entire fourth ventricle). A correlation was then sought between the quantitative values obtained by phase-contrast magnetic resonance imaging (PC-MRI) and the amount of signal void in the 3D-SPACE sequence. At the aqueduct level, there was a statistically significant difference in the forward flow velocity and the flow volume among different grades (all P < 0.001). In the grade 3 group, CSF peak systolic flow velocity and mean flow volume were found to be significantly higher than in the other grades (P < 0.001). The mean aqueduct area in the grade 0 group was found to be significantly different from that in the other classes (P < 0.001). The amount of signal loss in the fourth ventricle observed on T2W 3D-SPACE is correlated with the peak systolic velocity and flow volume measured quantitatively in PC-MRI.

The Vulnerability of Malagasy Protected Areas in the Face of Climate Change

This study examines the vulnerability of Madagascar’s protected areas (PAs) to climate change, focusing on climate change velocity, and its impact on biodiversity. We analyzed current and near future climate data using principal component analysis (PCA) and climate change velocity metrics to predict shifts in climatic conditions from the present to the near future, while under the mild and extreme emission scenarios (SSP 126, SSP 585). Forward velocities, which are characterized by the minimum distances that must be overcome by species to keep in track with their appropriate comparative climate, are most pronounced in western and southern Madagascar. In contrast, the backward velocity, which uses future climatic conditions in grid cells in comparison to current conditions, is more common in the eastern regions of the island, and hints at the minimum distance that organisms would have to overcome in colonizing a new habitat. Even though the correlations between PA size and climate change velocity are weak, there is a tendency for larger PAs to exhibit more stable climatic conditions. Conservation strategies must prioritize enhancing the resilience of PAs through adaptive management to mitigate climate impacts. Our findings provide crucial insights for policymakers and conservation planners to develop climate-smart strategies that ensure the long-term efficacy of Madagascar’s PA network.

Optimization of Operating Parameters for Straw Returning Machine Based on Vibration Characteristic Analysis

For the mechanized technical mode of total wheat straw returning to field, there are problems such as large vibration during the operation of the straw returning machine that, in turn, affect the effect of stubble breaking. This study took the Tongtian 1-JHY-220 straw returning machine as the research object to conduct field experiments, with wheat stubble height, forward velocity, and PTO speed as experimental parameters. And the vibration characteristics at different positions of the machine and the final stubble breaking rate were used as evaluation indicators. Combined with the orthogonal experiment and response surface analysis method, this article analyzes and discusses the influence of various parameters on vibration characteristics and operational effectiveness. The results show that PTO speed and wheat stubble height were the main factors affecting the vibration and operation quality of the straw returning machine. Low PTO speed and high stubble height can improve the stubble breaking rate of the straw returning machine and reduce its operation vibration. Furthermore, the multi-objective optimization results show that when the forward velocity in the range of 8.5–9 km/h, the PTO speed is 540 r/min, and the stubble height is in the range of 200–250 mm, the stubble breaking rate of the straw returning machine is greater than 86%. At this time, the total vibration of the straw returning machine and tractor rear axle is relatively small. This study can lay a foundation for further studying the impact of the vibration of the straw returning machine on the stubble breaking effect and provide a reference for the preparation of high-quality seedbed under conservation tillage.

Ferrimagnetic Tb/Co multilayers patterned by ion bombardment as substrates for magnetophoresis

Ion bombardment with 30 keV Ga+ ions can locally change the magnetic properties of perpendicular magnetic anisotropy ferrimagnetic Tb/Co based multilayers. The induced changes in the effective magnetization create high gradients of magnetic fields in the proximity of the perimeters of the bombarded areas. Superparamagnetic, micrometer-sized beads floating in an aqueous suspension over such a patterned structure respond to the ensuing magnetostatic energy landscape. This landscape, and its associated forces on the beads, can be controlled with a time varying, external, homogeneous magnetic field. It is shown that with a 3.37 kA/m (approx. 4.2 mT) field and switching the direction at 10 Hz frequencies the beads can be driven with average forward velocities reaching 40 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu$$\\end{document}m/s. This has the potential for use in lab-on-a-chip type assays.

Instrumentation in Tractor for Evaluating Performance of a Tractor Drawn Rotavator

Performance measurement of agricultural machinery is essential for enhancing their efficiency for which they are operated. The performance related data helps the operator to select optimum operating parameters which reduce fuel consumption and increase field productivity. This study was aimed to develop various sensing units and install them on the tractor for evaluating performance of a tractor drawn rotavator. A microcontroller based embedded system was developed for continuous recording of wheel slip, actual forward speed, throttle opening, engine speed, velocity ratio, draft and power take-off (PTO) torque related data while carrying out tillage operation with rotavator. The wheel slip (from 1.5% to 35%) and actual forward speed of the tractor (up to 6 km h-1) was measured using inductive proximity sensors. For measuring throttle opening (0 to 100%), engine speed (750 to 2500 rpm), draft (65 to 350 kg) and PTO torque (up to 420 N-m) values, a potentiometer, magnetic pickup sensor, S-type loadcell and PTO torque transducer, respectively, were used. For computing velocity ratio, peripheral speed of rotavator was measured using PTO speed data received from PTO torque transducer, and actual forward speed of tractor was measured using inductive proximity sensor. The developed sensing units were calibrated under both dynamic and static conditions, and were also verified in laboratory and field conditions. Results showed that output signals received from the sensing units varied linearly with applied load. The mean absolute percentage error (MAPE) between measured and actual values was found to be below 5%. From the measured parameters, specific energy requirement and total power required for carrying out tillage operation could be computed. The power requirement was found to increase with increase in both gear and throttle settings. The specific energy requirement was minimum when rotavator was operated in L2 gear of the tractor. The minimum specific energy of 21 Wh m-3 was found at L2 gear and 75% throttle opening.

Mapping Microplastic Movement: A Phase Diagram to Predict Nonbuoyant Microplastic Modes of Transport at the Particle Scale.

Microplastics pose numerous threats to aquatic environments, yet understanding their transport mechanisms remains limited. Drawing from natural sediment research provides valuable insights to address this knowledge gap. One key dimensionless number used to describe sediment transport is the transport stage, referring to the ratio between the flow shear velocity and the particle settling velocity. However, variations in physical properties, such as shape and density, raise concerns about the applicability of existing sediment transport theories to microplastics. To address this challenge, we employed a physical modeling approach, examining 24 different nonbuoyant microplastic particles in a turbulent open channel flow. Utilizing 3D particle tracking, a total of 720 trajectories were recorded and analyzed. Microplastic particles exhibited transport modes akin to natural sediments, including rolling/sliding, saltation, and suspension. The transport stage strongly correlated with these modes, as well as with the mean forward velocity and mean position in the water column. Notably, particle shape emerged as a critical factor influencing transport dynamics. Due to their lower settling velocity, fibers tended to stay closer to the water surface with lower forward velocities compared to spheres. Based on the laboratory results, a new phase diagram for microplastics is introduced analogous to an existing diagram for sediments.

Development of a Vertical Submerging and Emerging Bat-Ray-Inspired Underwater Vehicle.

In this article, the development of a bat-ray-inspired underwater vehicle is presented; although the propulsion of the vehicle is based on traditional thrusters, the shape of the ray's fins was used as a model to design the body of the vehicle; this architecture allows the independent control of the forward velocity and the full attitude of the vehicle using only two thrusters and two articulated fins. The compact design of the robot, along with the high dexterity of the architecture, allows the vehicle to submerge and emerge vertically as well as navigate horizontally. The mathematical model of the proposed vehicle, including dynamics and propulsion system, is presented and validated using numerical simulations. Finally, experimental tests are presented to demonstrate the capabilities of the proposed design.

Kinematic analysis and advanced control of a vectored thruster based on 3RRUR parallel manipulator for micro-size AUVs

Abstract Autonomous underwater vehicles (AUVs) have played a pivotal role in advancing ocean exploration and exploitation. However, traditional AUVs face limitations when executing missions at minimal or near-zero forward velocities due to the ineffectiveness of their control surfaces, considerably constraining their potential applications. To address this challenge, this paper introduces an innovative vectored thruster system based on a 3RRUR parallel manipulator tailored for micro-sized AUVs. The incorporation of a vectored thruster enhances the performance of micro-sized AUVs when operating at minimal and low forward speeds. A comprehensive exploration of the kinematics of the thrust-vectoring mechanism has been undertaken through theoretical analysis and experimental validation. The findings from theoretical analysis and experimental confirmation unequivocally affirm the feasibility of the devised thrust-vectoring mechanism. The precise control of the vector device is studied using Physics-informed Neural Network and Model Predictive Control (PINN-MPC). Through the adoption of this pioneering thrust-vectoring mechanism rooted in the 3RRUR parallel manipulator, AUVs can efficiently and effectively generate the requisite motion for thrust-vectoring propulsion, overcoming the limitations of traditional AUVs and expanding their potential applications across various domains.

Simulating vortex generation to investigate the propulsive and braking mechanisms of breaststroke kick using computational fluid dynamics on a breaststroke swimmer

Swimmers primarily increase their forward velocity through lower limb motion in breaststroke, making the breaststroke kick crucial for optimizing race times. Recent studies have highlighted the generation of vortices around the swimmer’s entire body to propel forward during swimming. However, the investigation of vortex generation during breaststroke kicks remains unexplored. This study aimed to reveal the propulsive and braking mechanisms of breaststroke kicks by simulating vortex generation using computational fluid dynamics (CFD). Kinematic data during the breaststroke kick and a three-dimensional digital model were collected to conduct CFD for a male breaststroke swimmer. Vortex generation was determined during one breaststroke kick from the CFD results. Vortices, which potentially induce a decrease in forward velocity, were generated by the swimmer’s lower legs and feet during the recovery phase. The swimmer generated vortices on the dorsal side of the feet and the posterior and lateral sides of the lower legs to increase the forward velocity during the out-sweep phase. The swimmer generated vortices on the lateral sides of the thighs and lower legs and the dorsal and lateral sides of the feet during the in-sweep phase to maintain forward velocity. Moreover, vortices generated from the out-sweep to the in-sweep merged and were shed backward relative to the swimming direction after the in-sweep phase. This study is the first to reveal the propulsive and braking mechanisms of breaststroke kicks by analyzing the vortex generation.

HCN channels are essential for the escape response of Paramecium.

When mechanical stimulation was applied to free swimming Paramecium, forward swimming velocity transiently increased due to activation of the posterior mechanosensory channels. The behavior response, known as "escape response," requires membrane hyperpolarization and the activation of K-channel type adenylate cyclases. Our hypothesis is that this escape response also involves activation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels. HCN channels are activated by hyperpolarization and are modulated by cyclic nucleotides such as cAMP and cGMP. They play a critical role in many excitable cells in higher animals. If HCN channels act in Paramecium, this should help to enhance and prolong hyperpolarization, thereby increasing the swimming speed of Paramecium. This study used RNAi to examine the role of the HCN channel 1 in the escape responses by generating hcn1-gene knockdown cells (hcn1-KD). These cells showed reduced mechanically-stimulated escape responses and a lack of cGMP-dependent increases in swimming speed. Electrophysiological experiments demonstrated reduced hyperpolarization upon injection of large negative currents in hcn1-KD cells. This is consistent with a decrease in HCN1 channel activity and changes in the escape response. These findings suggest that HCN1 channels are K+ channels that regulate the escape response of Paramecium by amplifying the hyperpolarizations elicited by posterior mechanical stimulation.

An experimental analysis of outdoor UAV localisation through diverse estimators and crowd-sensed data fusion

Motivated by the challenge of achieving precise 3D outdoor localisation for unmanned aerial vehicles (UAVs) in global navigation satellite system (GNSS)-denied environments, this paper introduces an innovative technique. Integrating crowd-sensed data fusion to counter inertial navigation system (INS) drift during GNSS signal outages, the proposed method exploits diverse estimators to enhance its efficacy. A micro lightweight frequency modulated continuous wave (FMCW) radar mounted on the UAV captures ground scatterer reflections, processed via fast Fourier transform (FFT) to generate a range-Doppler map. This map facilitates forward velocity estimation during GNSS signal loss. This approach employs adaptive thresholding, image binarisation, and connected components-based techniques for target detection from a computer vision standpoint. The derived radar-based velocity fuses with magnetometer, barometer, and inertial measurement unit (IMU) data using diverse estimators like extended Kalman filter (EKF) and particle filter (PF). Real-time flight data evaluation and simulated outage periods using EKF and PF validate the outdoor localisation system. Experimental analyses demonstrate substantial improvements, enhancing 3D positioning accuracy by 99.89% and 99.83% for the initial and subsequent flights, respectively, leveraging PF to fortify the standalone INS mode during GNSS signal loss. This approach significantly enhances UAV localisation precision, particularly in challenging GNSS-denied scenarios, showcasing the potential for real-world applications.

VLF oscillation mechanism analysis of underwater vehicle based on overlapping grid

ABSTRACT The present study aims to investigate the VLF oscillation mechanism of underwater vehicles motion by conducting numerical simulation on the AFF-1. The straight-line simulations of AFF-1 were performed using RANS equations with a Reynolds stress turbulence model at different speeds, and an initial flow field with converged resistance results was obtained. Meanwhile, the accuracy of the numerical model and input parameters were validated by the resistance results. The sinuous motion of AFF-1 was simulated based on dynamic fluid body interaction (DFBI) and overlapping grid. The results indicated that the inherent instability of the AFF-1 significantly impacts its motion dynamics. Specifically, when AFF-1was in straight-line motion at a constant forward velocity, it was subjected to the influence of pressure component of hydrodynamic force and moment, resulting in slow trajectory divergence motion of AFF-1. In scenarios where manual manipulation is employed, this instability leads to the emergence of VLF oscillations.

Framework for Numerical 6DOF Simulation with Focus on a Wing Deforming UAV in Perch Landing

The perch landing maneuver of a wing-deforming unmanned aerial vehicle (UAV) was investigated through a framework that uses the free, open-source OpenFOAM with 6 degrees of freedom (6DOF) simulations. The framework uses a moving grid to follow the trajectory of the UAV, reducing computational resources. Together with the ability to allow internal grid deformation, sliding mesh, and algorithm addition, it can accurately mimic the entire landing process. Different wing deformation speeds, additional elevator rotation and emulated propeller lift were added to the 6DOF simulations to investigate their effects on the landing maneuver. The results showed that the wing deformation retraction speed has a considerable effect on the trajectory and velocity of the UAV. The wing deformation reduced the forward velocity of the UAV by 32%, from 13.89 to 9 m/s. With the elevator control, the velocity was reduced to 5 m/s. Lastly, and an activation time of 1 s for the emulated propeller lift can further decrease the velocity to around 4.2 m/s. A better algorithm for the emulated propeller lift may be able to give a superior performance. This framework allows us to understand the underlying perch landing maneuver aerodynamics. It can also be used on problems like fast-turning agile and flapping wing flight.

Enhancing Swimming Performance of Magnetic Helical Microswimmers by Surface Microstructure.

Artificial bacterial flagella (ABF), also known as a magnetic helical microswimmer, has demonstrated enormous potential in various future biomedical applications (e.g., targeted drug delivery and minimally invasive surgery). Nevertheless, when used for in vivo/in vitro treatment applications, it is essential to achieve the high motion efficiency of the microswimmers for rapid therapy. In this paper, inspired by microorganisms, the surface microstructure was introduced into ABFs to investigate its effect on the swimming behavior. It was confirmed that compared with smooth counterparts, the ABF with surface microstructure reveals a smaller forward velocity below the step-out frequency (i.e., the frequency corresponding to the maximum velocity) but a larger maximum forward velocity and higher step-out frequency. A hydrodynamic model of microstructured ABF is employed to reveal the underlying movement mechanism, demonstrating that the interfacial slippage and the interaction between the fluid and the microstructure are essential to the swimming behavior. Furthermore, the effect of surface wettability and solid fraction of microstructure on the swimming performance of ABFs was investigated experimentally and analytically, which further reveals the influence of surface microstructure on the movement mechanism. The results present an effective approach for designing fast microrobots for in vivo/in vitro biomedical applications.

Optimal control for helicopter/turboshaft engine system based on hybrid variable speed

In order to address the limitation of fixed-ratio transmission (FRT), which compromises the attainment of both optimal main rotor speed and optimal power turbine speed, an optimal speed control method based on hybrid variable speed (HVS) is proposed. Firstly, based on the integrated performance calculation model of helicopter/turboshaft engine system, the distribution factors of variable speed are applied, and the integrated optimization method of optimal speed is proposed based on the minimum engine fuel flow. Subsequently, an online estimation technique employing a high-order filter is devised and engineered to achieve superior cascaded control of turboshaft engines. Finally, a novel real-time optimal speed control method based on hybrid variable speed is proposed. The simulation results under different operation conditions demonstrate that regardless of whether it is FRT or HVS, the optimal main rotor speed increases with forward velocity. In the case of HVS, turboshaft engine degradations have a significant impact on the optimal power turbine speed rather than optimal main rotor speed. Adopting an estimation method based on high-order filtering for gas turbine rotational acceleration proves more advantageous in mitigating high-frequency oscillation and continuous saltation of estimated values. Moreover, in comparison with the optimal speed control method of FRT, HVS-based approach enables simultaneous attainment of the optimal main rotor speed and power turbine speed, thereby enhancing the overall efficiency of the integrated helicopter/turboshaft engine system and significantly decreasing engine fuel consumption by over 2%. Consequently, there has been a remarkable enhancement in the overall performance of the integrated helicopter/turboshaft engine system.

Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle's Acceleration Measurements.

This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model's performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails' corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways.

Leading-edge curvature effect on aerodynamic performance of flapping wings in hover and forward flight

This study investigates the role of leading-edge (LE) curvature in flapping wing aerodynamics considering hovering and forward flight conditions. A scaled-up robotic model is towed along its longitudinal axis by a rack gear carriage system. The forward velocity of the robotic model is changed by varying the advance ratio J from 0 (hovering) to 1.0. The study reveals that the LE curvature has insignificant influence on the cycle-average aerodynamic lift and drag. However, the time-history lift coefficient shows that the curvature can enhance the lift around the middle of downstroke. This enhanced lift is reduced from 5% to 1.2% as J changed from 0 to 1.0. Further flow examinations reveal that the LE curvature is beneficial by enhancing circulation only at the outboard wing sections. The enhanced outboard circulation is found to emanate from the less stretched leading-edge vortices (LEVs), weakened trailing-edge vortices (TEVs), and the coherent merging of the tip vortices (TVs) with the minor LEVs as observed from the phase-lock planar digital particle image velocimetry measurements. The far-wake observation shows that the LE curvature enhances the vorticity within the TV, helping to reduce the overall flow fluctuations in the far field. These findings can be extended to explain the predominantly straight LE wing shape with a small amount of curvature only observed near the wing tip for flapping fliers with Re from 103 to 104.

Hydrodynamic performance analysis of swimming processes in self-propelled manta rays

To fill the research gap regarding the whole process (steady-state and nonsteady-state phases) of median and/or paired fin (MPF) mode swimming in underwater organisms, a two-degree-of-freedom self-propelled coupling method of motion and hydrodynamics based on user-defined functions of Fluent software was established, and numerical simulations were carried out for the startup, acceleration, and steady-state phases of manta rays. The interaction mechanism among the hydrodynamic characteristics, vortex evolution, and pressure distribution was investigated in the mentioned phases. We concluded that the negative pressure zone generated by the leading edge vortex and the shear layer contributes to thrust generation and changes in swimming velocity dominate the hydrodynamic characteristics by affecting the evolution of the shear layer and the leading edge vortex, with a 17.54% increase in forward average velocity in the fourth cycle compared to the third cycle and a consequent 9.5% increase in average thrust. In the end, the relationship between the formation of trailing edge vortex rings and changes in thrust was revealed. The vortex ring contributes to the increase in thrust, but the formation of the vortex ring comes at the cost of the loss of the leading edge vortex negative pressure zone, which greatly affects thrust, decreasing to 38.3% of its peak. The swimming mechanism revealed in this study provides a reference for the study of MPF-driven biodynamics and a new simulation strategy for the prediction of bionic navigator motions.

Parameter identification of an open-frame underwater vehicle based on numerical simulation and quantum particle swarm optimization

Accurate parameter identification of underwater vehicles is of great significance for their controller design and fault diagnosis. Some studies adopt numerical simulation methods to obtain the model parameters of underwater vehicles, but usually only conduct decoupled single-degree-of-freedom steady-state numerical simulations to identify resistance parameters. In this paper, the velocity response is solved by applying a force (or torque) to the underwater vehicle based on the overset grid and Dynamic Fluid-Body Interaction model of STAR-CCM+, solving for the velocity response of an underwater vehicle in all directions in response to propulsive force (or moment) inputs. Based on the data from numerical simulations, a parameter identification method using quantum particle swarm optimization is proposed to simultaneously identify inertia and resistance parameters. By comparing the forward velocity response curves obtained from pool experiments, the identified vehicle model’s mean square error of forward velocity is less than 0.20%, which is superior to the steady-state simulation method and particle swarm optimization and genetic algorithm approaches.

A backward cycling programme for people with Parkinson's disease: a feasibility and preliminary results study.

To assess the feasibility of backward cycling for people with Parkinson's disease. Secondary objectives were to assess changes in gait and balance following a 6-week program. A single-group prospective pre-test, post-test study with 1-month follow-up. Twenty-six people with Parkinson's disease (mean age: 69 (7.74) years, gender: 83% males, time since diagnosis: 6 (4.44) years). Participants pedaled backward on a stationary bicycle for 30 minutes at moderate intensity twice a week for 6 weeks. Feasibility was assessed by acceptability, suitability, and burden. Data collected at pre- and post-intervention with 1-month follow-up included backward stepping response variables, forward/backward gait variables, Mini-Balance Evaluation Systems Test (MBT), and 6 Minute Walk Test. There was a high retention rate (95.8%) and adherence rate (100%) with one adverse event and minimal burden. Significant improvements were seen in step count and excursion distance during backward stepping responses, forward and backward gait velocity, forward step length, and the Mini-BESTest. Backward cycling was a feasible intervention for people with Parkinson's disease, demonstrating low burden with high retention and adherence rates, and it is a safe exercise with the potential for benefits in gait and balance variables.